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14th International Symposium on Antennas Propagation and EM Theory

作者： Li Zhihao Zhang Ke Xiao Zhibin Li Pengpeng Ni Shaojie Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China Key Lab Satellite Nav Technol Changsha 410073 Peoples R China

ISBN: (数字)9798350353129

ISBN: (纸本)9798350353129;9798350353136

Long-wave antennas are extensively employed in extra long-distance communications and navigation, ocean surveillance, and radio broadcasting, owing to their superior propagation characteristics at low frequencies. Long-wave signals, characterized by strong penetrating power and extended propagation ranges, are well-suited for remote communications and signal transmission in unique environments. As information and communication technologies continue to evolve, the design and optimization of long-wave antennas have emerged as a focal research area. This article systematically reviews the fundamental principles, common types, design methodologies, and recent research advancements of long-wave antennas, aiming to offer a comprehensive reference framework to facilitate further advancements and applications in long-wave antenna technology.

关键词： Long wave antenna Antenna form Design methodology Numerical simulation optimization algorithm

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Vibration Attenuation of Rotating Blades Using a Nonlinear Energy Sink

Vibration Attenuation of Rotating Blades Using a Nonlinear E...

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AIAA Aviation Forum

作者： Wall, I. P. Amoozgar, M. Popov, A. A. Univ Nottingham Fac Engn Nottingham NG7 2RD England

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

The global climate crisis is placing higher demands on the performance of wind turbines. One of the promising methods to enhance the wind turbine performance is to increase the rotor diameter by having larger blades. However, the blade length is always constrained by its fatigue life due to vibratory loads or aeroelastic instabilities. To overcome this limitation, in this study a Nonlinear Energy Sink (NES) device is proposed to be integrated inside the blade to reduce the plunge and twist vibrations of the blade. NES devices are ineffective until an energy threshold of the primary system is exceeded at which point, they become highly effective at damping nonlinear structures. In this paper, this energy threshold correlates to the onset of flutter instability. One of the limitations of using a passive NES is that it could enhance the vibration of the system considerably in regions prior to flutter occurring if it is optimised for unstable damping. For this reason, this paper proposes the usage of a tuneable NES as an active vibration absorber that is capable of tuning to the optimum parameters of a given wind condition. A genetic algorithm has been used to demonstrate the effectiveness of tuning the NES to the optimum conditions for each flow speed. The Tuned NES (TNES) is shown to have an average amplitude reduction of about 19% across the range of flow speeds compared to 9% for the passive design with fixed optimum parameters.

关键词： Damping Coefficient Genetic algorithm Wind Turbine Aeroelastic Instability Fatigue Life Passive Control Aerodynamic Force Global Wind Energy Council optimization algorithm Activation Energy

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Verification of Image-Based Neural Network Controllers Using Generative Models

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2022年第9期19卷 574-584页

作者： Katz, Sydney M. Corso, Anthony L. Strong, Christopher A. Kochenderfer, Mykel J. Stanford Univ Dept Aeronaut & Astronaut Stanford CA 94305 USA Stanford Univ Dept Elect Engn Stanford CA 94305 USA

Although neural networks are effective tools for processing information from image-based sensors to produce control actions, their complex nature limits their use in safety-critical systems. For this reason, recent work has focused on combining techniques in formal methods and reachability analysis to obtain guarantees on the closed-loop performance of neural network controllers. However, these techniques do not scale to the high-dimensional and complicated input space of image-based neural network controllers. This work proposes a method to address these challenges by training a generative adversarial network to map states to plausible input images. Concatenating the generator network with the control network results in a network with a low-dimensional input space, which allows for the use of existing closed-loop verification tools to obtain formal guarantees on the performance of image-based controllers. This approach is applied to provide safety guarantees for an image-based neural network controller for an autonomous aircraft taxi problem. The resulting guarantees are with respect to the set of input images modeled by the generator network, and so a recall metric is provided to evaluate how well the generator captures the space of plausible images.

关键词： Generative Adversarial Network Proportional Control Sensors Aircraft Trajectory Perturbation Method Machine Learning optimization algorithm Flight Simulators Aviation Cessna

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Extended model predictive control scheme for smooth path following of autonomous vehicles

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Frontiers of Mechanical Engineering 2022年第1期17卷 34-49页

作者： Qianjie LIU Shuang SONG Huosheng HU Tengchao HUANG Chenyang LI Qingyuan ZHU Department of Mechanical and Electrical Engineering Xiamen UniversityXiamen 361102China School of Computer Science and Electronic Engineering University of EssexColchester CO43SQUK

This paper presents an extended model predictive control(MPC)scheme for implementing optimal path following of autonomous vehicles,which has multiple constraints and an integrated model of vehicle and road *** curvature and inclination factors are used in the construction of the vehicle dynamic model to describe its lateral and roll dynamics ***,rollover,and vehicle envelopes are used as multiple constraints in the MPC controller ***,an extended MPC method solved by differential evolutiona* optimization algorithm is proposed to realize optimal smooth path following based on driving path ***,simulation and real experiments are carried out to evaluate the feasibility and the effectiveness of the extended MPC *** indicate that the proposed method can obtain the smooth transition to follow the optimal drivable path and satisfy the lateral dynamic stability and environmental constraints,which can improve the path following quality for better ride comfort and road availability of autonomous vehicles.

关键词： autonomous vehicles vehicle dynamic modeling model predictive control path following optimization algorithm

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Geometric Design of Hypersonic Vehicles for Optimal Mission Performance with High-Fidelity Aerodynamic Models

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JOURNAL OF AIRCRAFT 2022年第3期60卷 870-882页

作者： Coulter, Brian G. Huang, Daning Wang, Zhenbo Univ Tennessee Dept Mech Aerosp & Biomed Engn Knoxville TN 37996 USA Penn State Univ Dept Aerosp Engn University Pk PA 16802 USA

Recent advances in efficienta* optimization algorithms and high-performance computing allow the construction of integrated design frameworks wherein the traditionally segregated disciplines such as airframe design, aerodynamics, and trajectory analysis can be coupled together in order to undertake the design and optimization of vehicles as integrated systems in a larger design space. The particular interest of this paper is a potential approach to incorporating high-fidelity aerodynamic models and trajectory optimization techniques in hypersonic vehicle designs by incrementally varying the geometric parameters of the vehicle to observe induced performance variations and optimize these parameters for specific mission profiles using data-driven optimization. First, the exigency of creating integrated design frameworks considering high-fidelity disciplinary models such as aerodynamics and trajectory modeling is justified. Then, an energy-based problem formulation for hypersonic trajectory optimization is introduced. A panel method based on the modified Newtonian flow theory and Eckert's reference model is used to produce high-fidelity aerodynamic force and heating coefficients, based on which a pseudospectral optimal control package is used to solve for optimal trajectories. Finally, a novel, iterative, data-driven framework employing Bayesian optimization and machine learning is established to integrate these disciplinary models and successively search for the geometry that enables the optimal mission performance. Simulation results demonstrate the feasibility and performance of the developed approach.

关键词： Aerodynamic Characteristics Pseudospectral Optimal Control Bayesian optimization optimization algorithm Aerodynamic Performance

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Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system

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ENERGY 2024年 292卷

作者： Zhang, Yagang Wang, Hui Wang, Jingchao Cheng, Xiaodan Wang, Tong Zhao, Zheng North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China North China Elect Power Univ Hebei Key Lab Phys & Energy Technol Baoding 071003 Peoples R China North China Elect Power Univ Dept Automat Baoding 071003 Peoples R China Univ South Carolina Interdisciplinary Math Inst Columbia SC 29208 USA North China Elect Power Univ Hebei Key Lab Phys & Energy Technol Box 205 Baoding 071003 Hebei Peoples R China

Wind power is a critical source of renewable energy, but the variability of wind speed can impact the efficiency and reliability of wind power generation. To improve the accuracy of wind speed prediction, this paper proposes a novel system that combines deep learning and decomposition algorithms based on ensemble optimization methods. Firstly, the paper proposes an innovative hybrid modal decomposition (HMD) method that extracts accurate features from wind speed data using Singular Value Decomposition (SVD), modal number selection method, and Variational Mode Decomposition (VMD). Secondly, the variable screening method identifies relevant factors affecting wind speed as fixed inputs. Then, the stationary test method is used to identify the sequences of different features in the mode and establish different prediction models: a bidirectional long-term short-time neural network model (Bi-LSTM) model optimized by artificial hummingbird algorithm (AHA) is used for non-stationary sequences;an ARIMA model is used for stationary sequences. Data from Sotavento and Changma wind farms are used to test this forecasting system. Simulation experiments are carried out from multiple dimensions. The results show that all error indicators are significantly improved, and the proposed prediction system is better than other comparative forecasting schemes.

关键词： Short-term wind forecasts Hybrid mode decomposition optimization algorithm Bi-directional long short-term memory Auto regressive integrated moving average model

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A comprehensive optimization framework for the design of high-performance building systems

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JOURNAL OF BUILDING ENGINEERING 2023年 65卷

作者： Zeng, Zhaoyun Lu, Di Hu, Yuqing Augenbroe, Godfried Chen, Jianli Argonne Natl Lab Lemont IL 60439 USA China Acad Bldg Res Beijing Peoples R China Penn State Univ State Coll PA 16801 USA Georgia Inst Technol Atlanta GA 30332 USA Univ Utah Salt Lake City UT 84112 USA

Improving the performance of buildings is crucial to achieving the Paris Agreement's goal of limiting global warming to 1.5 degrees C. optimization is critical to ensuring that buildings achieve the desired performance. In the literature, a systematic discussion on the interdependences between the objective functions, constraints, and design variables is missing. In this work, we try to fill this research gap by presenting a comprehensive optimization framework for the design of high-performance building systems. This framework provides a systematic and detailed introduction to each step of formulating and solving optimization problems. Two application studies are also presented to demonstrate how this optimization framework can be used in practical applications. The first study demonstrates how to construct cost models and handle optimization problems with economy objective functions, and the second study demonstrates how to solve multi-objective optimization problems with a-posteriori-articulation methods to generate the Pareto optimal set. This paper can serve as a guide for future researchers and designers by providing them with a basic problem-solving procedure and helping them avoid common mistakes.

关键词： Formulation optimization algorithm Multi-objective optimization Design variable Constraint Adaptive building envelope

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Optimum Operating Parameters and Blade Setting of a High-Speed Propeller

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JOURNAL OF AIRCRAFT 2022年第2期59卷 484-501页

作者： Boulkeraa, Tayeb Ghenaiet, Adel Benini, Ernesto Polytech Mil Sch Fac Mech Engn & Proc Engn Lab Energy Convers Syst Bordj El Bahri 16046 Algeria Univ Sci & Technol Houari Boumediene Lab Turbomachinery Fluid Mech & Energet Dept Bab Ezzouar 16111 Algeria Univ Padua Dept Ind Engn Via Venezia 1 I-35131 Padua Italy

Traditionally, the design of propellers is accomplished on the bases of engineering experience and the availability of well-established experimental data. However, the emergence of computational fluid dynamics (CFD) tools coupled with advanced optimizers has made this task easier. This paper presents the essentials of an optimization-based CFD chain aimed at finding the optimal operating parameters and blade settings of a high-speed propeller. This fully automated procedure combines an improved hypercube sampling (IHS) for the initial design of experiment and a radial basis function (RBF) as the meta-model, hence leading to lesser CFD simulations. The coupling between IHS and RBF is judiciously implemented, and the sampling strategy and updates are thoroughly examined. The optimization procedure uses the multi-objective evolutionary algorithm nondominated sorting genetic algorithm to determine the operating parameters and blade setting that maximize the propeller efficiency and thrust. At the optimum point the blade operates with mild pressure gradients, whereas the transonic region is restricted to the upper corner of the blade tip.

关键词： High Speed Propellers Large Eddy Simulation Multi Objective Evolutionary algorithms Genetic algorithm Design of Experiments Freestream Conditions Reynolds Averaged Navier Stokes Blade Element Momentum Theory Aerodynamic Efficiency optimization algorithm

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Multi-population evolutionary computing based multi-agent smart distribution system service restoration

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ELECTRICAL ENGINEERING 2022年第5期104卷 3295-3311页

作者： Lin, Yu-Hsiu Natl Taipei Univ Technol Grad Inst Automat Technol Taipei 106344 Taiwan

Distribution system service restoration is a process of restoring power outages through changing the on/off status of sectionalizing and tie switches within the whole power outage in a distribution system altered accordingly with its topological structure. Multi-agent Systems (MASs) can be applied for distribution system service restoration, an engineering optimization problem that can be addressed through metaheuristics, of a distribution system because service restoration planning can be made in parallel among intelligent software agents embedded inside sectionalizing and tie switches and feeders to form a distributed multi-agent environment such that the time of power restoration can be reduced. Service restoration planning can be built upon an MAS. This paper presents a three-tiered MAS-based Multi-Population Parallel Genetic algorithm (MPPGA) and demonstrates its preliminary implementation to achieve service restoration planning for smart distribution system service restoration, which serves as a meta service restoration planner to perform efficient and fast switching operation for smart distribution system service restoration. The presented three-tiered MAS-based MPPGA is implemented, in Java programming language, on a Java Agent DEvelopment (JADE for short) platform, where in evolutionary computation (1) multiple populations are coevolved and (2) selection, crossover, and mutation operations for genetic search are parallelized. The effectiveness/feasibility of the presented three-tiered MAS-based MPPGA is demonstrated by a simulated distribution system and reported with simulation results.

关键词： Artificial intelligence Distribution feeder reconfiguration JADE Multi-agent system optimization algorithm Smart grid

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An intelligent approach for estimating aeration efficiency in stepped cascades: optimized support vector regression models and mutual information theory

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SOFT COMPUTING 2022年第24期26卷 13969-13984页

作者： Asadollah, Seyed Babak Haji Seyed Sharafati, Ahmad Haghbin, Masoud Motta, Davide Noghani, Mohamadreza Hosseinian Moghadam SUNY Coll Environm Sci & Forestry Dept Environm Resources Engn Syracuse NY 13210 USA Univ Granada UGR Andalusian Res Inst Data Sci & Computat Intellige Dept Struct Mech & Hydraul Engn Granada 18001 Spain Northumbria Univ Dept Mech & Construct Engn Wynne Jones Bldg Newcastle Upon Tyne NE1 8ST Tyne & Wear England

Soft computing (SC) methods have increasingly been used to solve complex hydraulic engineering problems, especially those characterized by high uncertainty. SC approaches have previously proved to be an accurate tool for predicting the aeration efficiency coefficient (E20) in hydraulic structures such as weirs and flumes. In this study, the performance of the standalone support vector regression (SVR) algorithm and three of its hybrid versions, support vector regression-firefly algorithm (SVR-FA), support vector regression-grasshoppera* optimization algorithm (SVR-GOA), and support vector regression-artificial bee colony (SVR-ABC), is assessed for the prediction of E20 in stepped cascades. Mutual information theory is used to construct input variable combinations for prediction, including the parameters unit discharge (q), the total number of steps (N), step height (h), chute overall length (L), and chute inclination (alpha) Entropy indicators, such as maximum likelihood, Jeffrey, Laplace, Schurmann-Grassberger, and minimax, are computed to quantify the epistemic uncertainty associated with the models. Four indices-correlation coefficient (R), Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), and mean absolute error (MAE)-are employed for evaluating the models' prediction performance. The models' outputs reveal that the SVR-FA model (with R = 0:947;NSE = 0:888;RMSE = 0:048 and MAE = 0:027 in testing phase) has the best performance among all the models considered. The input variable combination, including q, N, h, and L, provides the best predictions with the SVR, SVR-FA, and SVR-GOA models. From the uncertainty analysis, the SVR-FA model shows the closest entropy values to the observed ones (3.630 vs. 3.628 for the "classic" entropy method and 3.647 vs. 3.643 on average for the Bayesian entropy method). This study proves that SC algorithms can be highly accurate in simulating aeration efficiency in stepped cascades and provide a valid alternative to the

关键词： Aeration efficiency Stepped cascades Support vector regression optimization algorithm

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